Minimum taxi fleet algorithm considering human spatiotemporal behaviors

Zhi-Dan Zhao; Yu Wang; Wei-Peng Nie; Chu-Yong Lin; Shi-Min Cai; Celso Grebogi

doi:10.1063/5.0152728

Minimum taxi fleet algorithm considering human spatiotemporal behaviors

Zhi-Dan Zhao^* (Corresponding Author), Yu Wang, Wei-Peng Nie, Chu-Yong Lin, Shi-Min Cai, Celso Grebogi^* (Corresponding Author)

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

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Abstract

With the development of information technology, more and more travel data have provided great convenience for scholars to study the travel behavior of users. Planning user travel has increasingly attracted researchers’ attention due to its great theoretical significance and practical value. In this study, we not only consider the minimum fleet size required to meet the urban travel needs but also consider the travel time and distance of the fleet. Based on the above reasons, we propose a travel scheduling solution that comprehensively considers time and space costs, namely, the Spatial-Temporal Hopcroft–Karp (STHK) algorithm. The analysis results show that the STHK algorithm not only significantly reduces the off-load time and off-load distance of the fleet travel by as much as 81 % and 58 % and retains the heterogeneous characteristics of human travel behavior. Our study indicates that the new planning algorithm provides the size of the fleet to meet the needs of urban travel and reduces the extra travel time and distance, thereby reducing energy consumption and reducing carbon dioxide emissions. Concurrently, the travel planning results also conform to the basic characteristics of human travel and have important theoretical significance and practical application value.

Original language	English
Article number	063158
Number of pages	12
Journal	Chaos
Volume	33
Issue number	6
Early online date	1 Jun 2023
DOIs	https://doi.org/10.1063/5.0152728
Publication status	Published - 30 Jun 2023

Bibliographical note

SUPPLEMENTARY MATERIAL
The green dot in the supplementary figure represents the time and distance relationship of a single off-load itinerary. The red line represents the mean distance grouped by time, and the red vertical line represents the standard deviation. There is a strong correlation between time and distance in off-load itineraries.

ACKNOWLEDGMENTS
This work was supported by the National Natural Science Foundation of China (Grant No. 62176148), the Scientific Research Foundation of Shantou University (Grant No. NTF19015), the 2020 Li Ka Shing Foundation Cross-Disciplinary Research (Grant No. 2020LKSFG09D), the Science Promotion Program of UESTC, China (No. Y03111023901014006), the Guangdong Basic and Applied Basic Research Foundation (Grant No. 2021A1515012294), and the Basic and Applied Basic Research of Colleges and Universities in Guangdong Province (Special Projects in Digital Economic: 2021ZDZX3025). The funders had no role in the study design, data collection, analysis, the decision to publish, or the preparation of the manuscript.

Data Availability Statement

The data and relevant codes that support the findings of this study are available from the corresponding authors upon reasonable request.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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Cite this

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title = "Minimum taxi fleet algorithm considering human spatiotemporal behaviors",

abstract = "With the development of information technology, more and more travel data have provided great convenience for scholars to study the travel behavior of users. Planning user travel has increasingly attracted researchers{\textquoteright} attention due to its great theoretical significance and practical value. In this study, we not only consider the minimum fleet size required to meet the urban travel needs but also consider the travel time and distance of the fleet. Based on the above reasons, we propose a travel scheduling solution that comprehensively considers time and space costs, namely, the Spatial-Temporal Hopcroft–Karp (STHK) algorithm. The analysis results show that the STHK algorithm not only significantly reduces the off-load time and off-load distance of the fleet travel by as much as 81 % and 58 % and retains the heterogeneous characteristics of human travel behavior. Our study indicates that the new planning algorithm provides the size of the fleet to meet the needs of urban travel and reduces the extra travel time and distance, thereby reducing energy consumption and reducing carbon dioxide emissions. Concurrently, the travel planning results also conform to the basic characteristics of human travel and have important theoretical significance and practical application value.",

author = "Zhi-Dan Zhao and Yu Wang and Wei-Peng Nie and Chu-Yong Lin and Shi-Min Cai and Celso Grebogi",

note = "SUPPLEMENTARY MATERIAL The green dot in the supplementary figure represents the time and distance relationship of a single off-load itinerary. The red line represents the mean distance grouped by time, and the red vertical line represents the standard deviation. There is a strong correlation between time and distance in off-load itineraries. ACKNOWLEDGMENTS This work was supported by the National Natural Science Foundation of China (Grant No. 62176148), the Scientific Research Foundation of Shantou University (Grant No. NTF19015), the 2020 Li Ka Shing Foundation Cross-Disciplinary Research (Grant No. 2020LKSFG09D), the Science Promotion Program of UESTC, China (No. Y03111023901014006), the Guangdong Basic and Applied Basic Research Foundation (Grant No. 2021A1515012294), and the Basic and Applied Basic Research of Colleges and Universities in Guangdong Province (Special Projects in Digital Economic: 2021ZDZX3025). The funders had no role in the study design, data collection, analysis, the decision to publish, or the preparation of the manuscript.",

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AU - Grebogi, Celso

N1 - SUPPLEMENTARY MATERIAL The green dot in the supplementary figure represents the time and distance relationship of a single off-load itinerary. The red line represents the mean distance grouped by time, and the red vertical line represents the standard deviation. There is a strong correlation between time and distance in off-load itineraries. ACKNOWLEDGMENTS This work was supported by the National Natural Science Foundation of China (Grant No. 62176148), the Scientific Research Foundation of Shantou University (Grant No. NTF19015), the 2020 Li Ka Shing Foundation Cross-Disciplinary Research (Grant No. 2020LKSFG09D), the Science Promotion Program of UESTC, China (No. Y03111023901014006), the Guangdong Basic and Applied Basic Research Foundation (Grant No. 2021A1515012294), and the Basic and Applied Basic Research of Colleges and Universities in Guangdong Province (Special Projects in Digital Economic: 2021ZDZX3025). The funders had no role in the study design, data collection, analysis, the decision to publish, or the preparation of the manuscript.

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N2 - With the development of information technology, more and more travel data have provided great convenience for scholars to study the travel behavior of users. Planning user travel has increasingly attracted researchers’ attention due to its great theoretical significance and practical value. In this study, we not only consider the minimum fleet size required to meet the urban travel needs but also consider the travel time and distance of the fleet. Based on the above reasons, we propose a travel scheduling solution that comprehensively considers time and space costs, namely, the Spatial-Temporal Hopcroft–Karp (STHK) algorithm. The analysis results show that the STHK algorithm not only significantly reduces the off-load time and off-load distance of the fleet travel by as much as 81 % and 58 % and retains the heterogeneous characteristics of human travel behavior. Our study indicates that the new planning algorithm provides the size of the fleet to meet the needs of urban travel and reduces the extra travel time and distance, thereby reducing energy consumption and reducing carbon dioxide emissions. Concurrently, the travel planning results also conform to the basic characteristics of human travel and have important theoretical significance and practical application value.

AB - With the development of information technology, more and more travel data have provided great convenience for scholars to study the travel behavior of users. Planning user travel has increasingly attracted researchers’ attention due to its great theoretical significance and practical value. In this study, we not only consider the minimum fleet size required to meet the urban travel needs but also consider the travel time and distance of the fleet. Based on the above reasons, we propose a travel scheduling solution that comprehensively considers time and space costs, namely, the Spatial-Temporal Hopcroft–Karp (STHK) algorithm. The analysis results show that the STHK algorithm not only significantly reduces the off-load time and off-load distance of the fleet travel by as much as 81 % and 58 % and retains the heterogeneous characteristics of human travel behavior. Our study indicates that the new planning algorithm provides the size of the fleet to meet the needs of urban travel and reduces the extra travel time and distance, thereby reducing energy consumption and reducing carbon dioxide emissions. Concurrently, the travel planning results also conform to the basic characteristics of human travel and have important theoretical significance and practical application value.

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Minimum taxi fleet algorithm considering human spatiotemporal behaviors

Abstract

Bibliographical note

Data Availability Statement

UN SDGs

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